Determination of seismic liquefaction potential of soil based on strain energy concept

Abstract

In the present study, minimax probability machine regression (MPMR) and extreme learning machine (ELM) have been adopted for prediction of seismic liquefaction of soil based on strain energy. Initial effective mean confining pressure ( $$ \sigma_{\text{mean}}^{\prime} $$ ), initial relative density after consolidation (D r), percentage of fines content (FC), coefficient of uniformity (C u), and mean grain size (D 50) have been taken as inputs of MPMR and ELM models. MPMR and ELM have been used as regression techniques. The performances of MPMR and ELM have been compared with the artificial neural network. A sensitivity analysis has been carried out to determine the effect of each input. The experimental results demonstrate that proposed methods are robust models for determination seismic liquefaction potential of soil based on strain energy.